Overview

Dataset statistics

Number of variables19
Number of observations6398
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory949.8 KiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 5966 distinct valuesHigh cardinality
artist has a high cardinality: 3355 distinct valuesHigh cardinality
uri has a high cardinality: 6378 distinct valuesHigh cardinality
energy is highly overall correlated with danceability and 4 other fieldsHigh correlation
loudness is highly overall correlated with energy and 2 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
instrumentalness is highly overall correlated with energy and 2 other fieldsHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
target is highly overall correlated with danceability and 2 other fieldsHigh correlation
danceability is highly overall correlated with energy and 2 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 715 (11.2%) zerosZeros
instrumentalness has 2406 (37.6%) zerosZeros

Reproduction

Analysis started2022-11-29 22:51:54.345642
Analysis finished2022-11-29 22:52:11.691046
Duration17.35 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5966
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
Falling
 
13
Chebika - Claudio Mate Metal Remix
 
8
Stay
 
7
Monster
 
6
Feeling Punk - Asle Remix
 
6
Other values (5961)
6358 

Length

Max length109
Median length79
Mean length15.701
Min length1

Characters and Unicode

Total characters100455
Distinct characters157
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5638 ?
Unique (%)88.1%

Sample

1st rowWild Things
2nd rowSurfboard
3rd rowLove Someone
4th rowMusic To My Ears (feat. Tory Lanez)
5th rowJuju On That Beat (TZ Anthem)

Common Values

ValueCountFrequency (%)
Falling 13
 
0.2%
Chebika - Claudio Mate Metal Remix 8
 
0.1%
Stay 7
 
0.1%
Monster 6
 
0.1%
Feeling Punk - Asle Remix 6
 
0.1%
Until the Day I Die 5
 
0.1%
X 5
 
0.1%
Metal 4
 
0.1%
Home 4
 
0.1%
Loungin' 4
 
0.1%
Other values (5956) 6336
99.0%

Length

2022-11-29T17:52:11.764191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 684
 
3.5%
609
 
3.1%
you 338
 
1.7%
of 278
 
1.4%
i 260
 
1.3%
me 235
 
1.2%
a 219
 
1.1%
it 196
 
1.0%
metal 192
 
1.0%
in 181
 
0.9%
Other values (5354) 16169
83.5%

Most occurring characters

ValueCountFrequency (%)
12965
 
12.9%
e 9212
 
9.2%
o 6112
 
6.1%
a 5837
 
5.8%
i 5033
 
5.0%
n 4903
 
4.9%
t 4891
 
4.9%
r 4393
 
4.4%
l 3594
 
3.6%
s 3239
 
3.2%
Other values (147) 40276
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65784
65.5%
Uppercase Letter 18178
 
18.1%
Space Separator 12965
 
12.9%
Other Punctuation 1476
 
1.5%
Decimal Number 656
 
0.7%
Dash Punctuation 518
 
0.5%
Close Punctuation 405
 
0.4%
Open Punctuation 405
 
0.4%
Other Letter 38
 
< 0.1%
Currency Symbol 9
 
< 0.1%
Other values (7) 21
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9212
14.0%
o 6112
 
9.3%
a 5837
 
8.9%
i 5033
 
7.7%
n 4903
 
7.5%
t 4891
 
7.4%
r 4393
 
6.7%
l 3594
 
5.5%
s 3239
 
4.9%
h 2571
 
3.9%
Other values (37) 15999
24.3%
Other Letter
ValueCountFrequency (%)
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (27) 27
71.1%
Uppercase Letter
ValueCountFrequency (%)
T 1548
 
8.5%
M 1443
 
7.9%
S 1417
 
7.8%
B 1146
 
6.3%
I 1074
 
5.9%
L 1035
 
5.7%
A 1027
 
5.6%
D 974
 
5.4%
W 846
 
4.7%
C 832
 
4.6%
Other values (22) 6836
37.6%
Other Punctuation
ValueCountFrequency (%)
' 523
35.4%
. 343
23.2%
, 197
 
13.3%
& 89
 
6.0%
" 74
 
5.0%
: 66
 
4.5%
/ 56
 
3.8%
? 35
 
2.4%
! 31
 
2.1%
* 26
 
1.8%
Other values (5) 36
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 167
25.5%
0 134
20.4%
2 96
14.6%
4 49
 
7.5%
3 44
 
6.7%
8 43
 
6.6%
9 37
 
5.6%
6 30
 
4.6%
7 29
 
4.4%
5 27
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 517
99.8%
1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 394
97.3%
] 11
 
2.7%
Open Punctuation
ValueCountFrequency (%)
( 394
97.3%
[ 11
 
2.7%
Math Symbol
ValueCountFrequency (%)
+ 4
57.1%
> 3
42.9%
Space Separator
ValueCountFrequency (%)
12965
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 9
100.0%
Final Punctuation
ValueCountFrequency (%)
8
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83962
83.6%
Common 16454
 
16.4%
Han 24
 
< 0.1%
Katakana 8
 
< 0.1%
Hiragana 6
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9212
 
11.0%
o 6112
 
7.3%
a 5837
 
7.0%
i 5033
 
6.0%
n 4903
 
5.8%
t 4891
 
5.8%
r 4393
 
5.2%
l 3594
 
4.3%
s 3239
 
3.9%
h 2571
 
3.1%
Other values (69) 34177
40.7%
Common
ValueCountFrequency (%)
12965
78.8%
' 523
 
3.2%
- 517
 
3.1%
) 394
 
2.4%
( 394
 
2.4%
. 343
 
2.1%
, 197
 
1.2%
1 167
 
1.0%
0 134
 
0.8%
2 96
 
0.6%
Other values (30) 724
 
4.4%
Han
ValueCountFrequency (%)
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (14) 14
58.3%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Inherited
ValueCountFrequency (%)
̃ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100299
99.8%
None 105
 
0.1%
CJK 24
 
< 0.1%
Punctuation 11
 
< 0.1%
Katakana 9
 
< 0.1%
Hiragana 6
 
< 0.1%
Diacriticals 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12965
 
12.9%
e 9212
 
9.2%
o 6112
 
6.1%
a 5837
 
5.8%
i 5033
 
5.0%
n 4903
 
4.9%
t 4891
 
4.9%
r 4393
 
4.4%
l 3594
 
3.6%
s 3239
 
3.2%
Other values (75) 40120
40.0%
None
ValueCountFrequency (%)
é 27
25.7%
ó 13
12.4%
ä 11
10.5%
å 6
 
5.7%
á 4
 
3.8%
ü 4
 
3.8%
í 4
 
3.8%
ã 3
 
2.9%
à 3
 
2.9%
ñ 3
 
2.9%
Other values (20) 27
25.7%
Punctuation
ValueCountFrequency (%)
8
72.7%
2
 
18.2%
1
 
9.1%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
CJK
ValueCountFrequency (%)
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (14) 14
58.3%
Katakana
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Diacriticals
ValueCountFrequency (%)
̃ 1
100.0%

artist
Categorical

Distinct3355
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
Drake
 
50
Glee Cast
 
41
Taylor Swift
 
35
Luke Bryan
 
25
Johann Sebastian Bach
 
25
Other values (3350)
6222 

Length

Max length88
Median length71
Mean length15.274148
Min length2

Characters and Unicode

Total characters97724
Distinct characters121
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2460 ?
Unique (%)38.4%

Sample

1st rowAlessia Cara
2nd rowEsquivel!
3rd rowLukas Graham
4th rowKeys N Krates
5th rowZay Hilfigerrr & Zayion McCall

Common Values

ValueCountFrequency (%)
Drake 50
 
0.8%
Glee Cast 41
 
0.6%
Taylor Swift 35
 
0.5%
Luke Bryan 25
 
0.4%
Johann Sebastian Bach 25
 
0.4%
The Weeknd 24
 
0.4%
Post Malone 23
 
0.4%
Hiphop Tamizha 23
 
0.4%
Alexandre Desplat 22
 
0.3%
Jason Aldean 22
 
0.3%
Other values (3345) 6108
95.5%

Length

2022-11-29T17:52:11.876275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
featuring 901
 
5.5%
491
 
3.0%
the 413
 
2.5%
lil 150
 
0.9%
drake 141
 
0.9%
chris 111
 
0.7%
brown 97
 
0.6%
of 80
 
0.5%
black 78
 
0.5%
nicki 76
 
0.5%
Other values (3752) 13744
84.4%

Most occurring characters

ValueCountFrequency (%)
9884
 
10.1%
e 8715
 
8.9%
a 7963
 
8.1%
i 6183
 
6.3%
n 5959
 
6.1%
r 5778
 
5.9%
o 4523
 
4.6%
t 4058
 
4.2%
l 3879
 
4.0%
s 3100
 
3.2%
Other values (111) 37682
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69211
70.8%
Uppercase Letter 17171
 
17.6%
Space Separator 9884
 
10.1%
Other Punctuation 927
 
0.9%
Decimal Number 306
 
0.3%
Dash Punctuation 95
 
0.1%
Other Letter 51
 
0.1%
Currency Symbol 43
 
< 0.1%
Math Symbol 21
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8715
12.6%
a 7963
11.5%
i 6183
 
8.9%
n 5959
 
8.6%
r 5778
 
8.3%
o 4523
 
6.5%
t 4058
 
5.9%
l 3879
 
5.6%
s 3100
 
4.5%
u 2814
 
4.1%
Other values (32) 16239
23.5%
Uppercase Letter
ValueCountFrequency (%)
F 1367
 
8.0%
B 1346
 
7.8%
T 1261
 
7.3%
S 1260
 
7.3%
C 1136
 
6.6%
M 1110
 
6.5%
D 1089
 
6.3%
L 914
 
5.3%
A 867
 
5.0%
J 783
 
4.6%
Other values (20) 6038
35.2%
Other Letter
ValueCountFrequency (%)
7
13.7%
7
13.7%
6
 
11.8%
4
 
7.8%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
Other values (11) 15
29.4%
Other Punctuation
ValueCountFrequency (%)
& 461
49.7%
. 216
23.3%
, 150
 
16.2%
' 43
 
4.6%
! 39
 
4.2%
/ 8
 
0.9%
* 5
 
0.5%
" 3
 
0.3%
: 1
 
0.1%
· 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 72
23.5%
1 45
14.7%
5 43
14.1%
3 31
10.1%
9 29
9.5%
6 26
 
8.5%
0 20
 
6.5%
8 20
 
6.5%
4 11
 
3.6%
7 9
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 10
83.3%
] 2
 
16.7%
Open Punctuation
ValueCountFrequency (%)
[ 2
66.7%
( 1
33.3%
Space Separator
ValueCountFrequency (%)
9884
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 43
100.0%
Math Symbol
ValueCountFrequency (%)
+ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86382
88.4%
Common 11291
 
11.6%
Katakana 34
 
< 0.1%
Han 17
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8715
 
10.1%
a 7963
 
9.2%
i 6183
 
7.2%
n 5959
 
6.9%
r 5778
 
6.7%
o 4523
 
5.2%
t 4058
 
4.7%
l 3879
 
4.5%
s 3100
 
3.6%
u 2814
 
3.3%
Other values (62) 33410
38.7%
Common
ValueCountFrequency (%)
9884
87.5%
& 461
 
4.1%
. 216
 
1.9%
, 150
 
1.3%
- 95
 
0.8%
2 72
 
0.6%
1 45
 
0.4%
5 43
 
0.4%
$ 43
 
0.4%
' 43
 
0.4%
Other values (18) 239
 
2.1%
Katakana
ValueCountFrequency (%)
6
17.6%
4
11.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
Other values (6) 8
23.5%
Han
ValueCountFrequency (%)
7
41.2%
7
41.2%
1
 
5.9%
1
 
5.9%
1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97589
99.9%
None 84
 
0.1%
Katakana 34
 
< 0.1%
CJK 17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9884
 
10.1%
e 8715
 
8.9%
a 7963
 
8.2%
i 6183
 
6.3%
n 5959
 
6.1%
r 5778
 
5.9%
o 4523
 
4.6%
t 4058
 
4.2%
l 3879
 
4.0%
s 3100
 
3.2%
Other values (69) 37547
38.5%
None
ValueCountFrequency (%)
é 18
21.4%
á 13
15.5%
ä 11
13.1%
ö 10
11.9%
ó 7
 
8.3%
í 4
 
4.8%
ü 3
 
3.6%
ð 3
 
3.6%
ï 2
 
2.4%
ê 2
 
2.4%
Other values (11) 11
13.1%
CJK
ValueCountFrequency (%)
7
41.2%
7
41.2%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Katakana
ValueCountFrequency (%)
6
17.6%
4
11.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
Other values (6) 8
23.5%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6378
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
spotify:track:0iA1unTbTbDOWUSlbwJ1pS
 
2
spotify:track:7zBQRGpYImAdIZc97FNj3V
 
2
spotify:track:7EZPH9Px3gXlxD5KJDwtwc
 
2
spotify:track:4Km5HrUvYTaSUfiSGPJeQR
 
2
spotify:track:69dXVFCMiz3SL1L7M24NdX
 
2
Other values (6373)
6388 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters230328
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6358 ?
Unique (%)99.4%

Sample

1st rowspotify:track:2ZyuwVvV6Z3XJaXIFbspeE
2nd rowspotify:track:61APOtq25SCMuK0V5w2Kgp
3rd rowspotify:track:2JqnpexlO9dmvjUMCaLCLJ
4th rowspotify:track:0cjfLhk8WJ3etPTCseKXtk
5th rowspotify:track:1lItf5ZXJc1by9SbPeljFd

Common Values

ValueCountFrequency (%)
spotify:track:0iA1unTbTbDOWUSlbwJ1pS 2
 
< 0.1%
spotify:track:7zBQRGpYImAdIZc97FNj3V 2
 
< 0.1%
spotify:track:7EZPH9Px3gXlxD5KJDwtwc 2
 
< 0.1%
spotify:track:4Km5HrUvYTaSUfiSGPJeQR 2
 
< 0.1%
spotify:track:69dXVFCMiz3SL1L7M24NdX 2
 
< 0.1%
spotify:track:5KONnBIQ9LqCxyeSPin26k 2
 
< 0.1%
spotify:track:34HwOOG2tTiz6tAN9h83YT 2
 
< 0.1%
spotify:track:21jGcNKet2qwijlDFuPiPb 2
 
< 0.1%
spotify:track:4jtyUzZm9WLc2AdaJ1dso7 2
 
< 0.1%
spotify:track:2fQ6sBFWaLv2Gxos4igHLy 2
 
< 0.1%
Other values (6368) 6378
99.7%

Length

2022-11-29T17:52:11.967933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:0ia1untbtbdowuslbwj1ps 2
 
< 0.1%
spotify:track:7fcnuwi6ufldtq08srxmzk 2
 
< 0.1%
spotify:track:7zbqrgpyimadizc97fnj3v 2
 
< 0.1%
spotify:track:0jxxngljqupsjazsgsbmzv 2
 
< 0.1%
spotify:track:047fcsbo4ndmwcbn8pcuxl 2
 
< 0.1%
spotify:track:6w9w8dsswa8knegvl3w97v 2
 
< 0.1%
spotify:track:6qn9ylkt13agvpq9jfo8py 2
 
< 0.1%
spotify:track:3n5oietjestsb71tjtfkwv 2
 
< 0.1%
spotify:track:1genui6m825v8jp4ukiiah 2
 
< 0.1%
spotify:track:2gwkd6igehqbdqegrccdob 2
 
< 0.1%
Other values (6368) 6378
99.7%

Most occurring characters

ValueCountFrequency (%)
t 15000
 
6.5%
: 12796
 
5.6%
p 8647
 
3.8%
r 8613
 
3.7%
y 8611
 
3.7%
s 8602
 
3.7%
k 8602
 
3.7%
i 8583
 
3.7%
a 8572
 
3.7%
f 8570
 
3.7%
Other values (53) 133732
58.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 133334
57.9%
Uppercase Letter 56057
24.3%
Decimal Number 28141
 
12.2%
Other Punctuation 12796
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 15000
 
11.2%
p 8647
 
6.5%
r 8613
 
6.5%
y 8611
 
6.5%
s 8602
 
6.5%
k 8602
 
6.5%
i 8583
 
6.4%
a 8572
 
6.4%
f 8570
 
6.4%
o 8560
 
6.4%
Other values (16) 40974
30.7%
Uppercase Letter
ValueCountFrequency (%)
A 2260
 
4.0%
H 2239
 
4.0%
I 2232
 
4.0%
J 2217
 
4.0%
C 2189
 
3.9%
W 2186
 
3.9%
O 2184
 
3.9%
N 2177
 
3.9%
Q 2176
 
3.9%
T 2163
 
3.9%
Other values (16) 34034
60.7%
Decimal Number
ValueCountFrequency (%)
2 3049
10.8%
5 3045
10.8%
6 3025
10.7%
1 2991
10.6%
3 2973
10.6%
0 2966
10.5%
4 2882
10.2%
7 2829
10.1%
8 2199
7.8%
9 2182
7.8%
Other Punctuation
ValueCountFrequency (%)
: 12796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 189391
82.2%
Common 40937
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 15000
 
7.9%
p 8647
 
4.6%
r 8613
 
4.5%
y 8611
 
4.5%
s 8602
 
4.5%
k 8602
 
4.5%
i 8583
 
4.5%
a 8572
 
4.5%
f 8570
 
4.5%
o 8560
 
4.5%
Other values (42) 97031
51.2%
Common
ValueCountFrequency (%)
: 12796
31.3%
2 3049
 
7.4%
5 3045
 
7.4%
6 3025
 
7.4%
1 2991
 
7.3%
3 2973
 
7.3%
0 2966
 
7.2%
4 2882
 
7.0%
7 2829
 
6.9%
8 2199
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 15000
 
6.5%
: 12796
 
5.6%
p 8647
 
3.8%
r 8613
 
3.7%
y 8611
 
3.7%
s 8602
 
3.7%
k 8602
 
3.7%
i 8583
 
3.7%
a 8572
 
3.7%
f 8570
 
3.7%
Other values (53) 133732
58.1%

danceability
Real number (ℝ)

Distinct882
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56816279
Minimum0.0622
Maximum0.981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:12.067946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0622
5-th percentile0.20185
Q10.447
median0.588
Q30.71
95-th percentile0.855
Maximum0.981
Range0.9188
Interquartile range (IQR)0.263

Descriptive statistics

Standard deviation0.1911025
Coefficient of variation (CV)0.33635167
Kurtosis-0.41606315
Mean0.56816279
Median Absolute Deviation (MAD)0.131
Skewness-0.3795124
Sum3635.1055
Variance0.036520166
MonotonicityNot monotonic
2022-11-29T17:52:12.161438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.624 24
 
0.4%
0.739 22
 
0.3%
0.531 22
 
0.3%
0.652 22
 
0.3%
0.507 21
 
0.3%
0.657 20
 
0.3%
0.599 20
 
0.3%
0.598 20
 
0.3%
0.596 20
 
0.3%
0.56 20
 
0.3%
Other values (872) 6187
96.7%
ValueCountFrequency (%)
0.0622 1
< 0.1%
0.0625 1
< 0.1%
0.0634 1
< 0.1%
0.0647 1
< 0.1%
0.0657 1
< 0.1%
0.0669 1
< 0.1%
0.0691 1
< 0.1%
0.0696 1
< 0.1%
0.0704 1
< 0.1%
0.0707 1
< 0.1%
ValueCountFrequency (%)
0.981 1
< 0.1%
0.98 1
< 0.1%
0.978 1
< 0.1%
0.974 2
< 0.1%
0.972 1
< 0.1%
0.969 1
< 0.1%
0.968 2
< 0.1%
0.965 1
< 0.1%
0.964 2
< 0.1%
0.962 1
< 0.1%

energy
Real number (ℝ)

Distinct1066
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66775647
Minimum0.000251
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:12.263164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000251
5-th percentile0.14685
Q10.533
median0.7125
Q30.857
95-th percentile0.972
Maximum0.999
Range0.998749
Interquartile range (IQR)0.324

Descriptive statistics

Standard deviation0.24072146
Coefficient of variation (CV)0.36049288
Kurtosis0.17155576
Mean0.66775647
Median Absolute Deviation (MAD)0.1575
Skewness-0.86059884
Sum4272.3059
Variance0.057946819
MonotonicityNot monotonic
2022-11-29T17:52:12.365839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.947 21
 
0.3%
0.772 21
 
0.3%
0.621 21
 
0.3%
0.931 20
 
0.3%
0.977 20
 
0.3%
0.791 20
 
0.3%
0.73 19
 
0.3%
0.665 19
 
0.3%
0.989 18
 
0.3%
0.882 18
 
0.3%
Other values (1056) 6201
96.9%
ValueCountFrequency (%)
0.000251 1
< 0.1%
0.000992 1
< 0.1%
0.00183 1
< 0.1%
0.00191 1
< 0.1%
0.00209 1
< 0.1%
0.00248 1
< 0.1%
0.00262 1
< 0.1%
0.00268 1
< 0.1%
0.00281 1
< 0.1%
0.00333 1
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.998 9
0.1%
0.997 14
0.2%
0.996 9
0.1%
0.995 10
0.2%
0.994 6
 
0.1%
0.993 11
0.2%
0.992 12
0.2%
0.991 16
0.3%
0.99 8
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2835261
Minimum0
Maximum11
Zeros715
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:12.447190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6062164
Coefficient of variation (CV)0.68253971
Kurtosis-1.298403
Mean5.2835261
Median Absolute Deviation (MAD)3
Skewness0.012894176
Sum33804
Variance13.004797
MonotonicityNot monotonic
2022-11-29T17:52:12.518863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 751
11.7%
0 715
11.2%
7 682
10.7%
2 584
9.1%
11 572
8.9%
9 560
8.8%
6 513
8.0%
5 507
7.9%
4 452
7.1%
8 445
7.0%
Other values (2) 617
9.6%
ValueCountFrequency (%)
0 715
11.2%
1 751
11.7%
2 584
9.1%
3 196
 
3.1%
4 452
7.1%
5 507
7.9%
6 513
8.0%
7 682
10.7%
8 445
7.0%
9 560
8.8%
ValueCountFrequency (%)
11 572
8.9%
10 421
6.6%
9 560
8.8%
8 445
7.0%
7 682
10.7%
6 513
8.0%
5 507
7.9%
4 452
7.1%
3 196
 
3.1%
2 584
9.1%

loudness
Real number (ℝ)

Distinct4704
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.5897963
Minimum-46.655
Maximum-0.149
Zeros0
Zeros (%)0.0%
Negative6398
Negative (%)100.0%
Memory size50.1 KiB
2022-11-29T17:52:12.598390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-46.655
5-th percentile-18.56195
Q1-8.425
median-6.0965
Q3-4.60125
95-th percentile-3.01485
Maximum-0.149
Range46.506
Interquartile range (IQR)3.82375

Descriptive statistics

Standard deviation5.2345923
Coefficient of variation (CV)-0.68968811
Kurtosis8.9333836
Mean-7.5897963
Median Absolute Deviation (MAD)1.7645
Skewness-2.6684045
Sum-48559.517
Variance27.400956
MonotonicityNot monotonic
2022-11-29T17:52:12.700985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17.135 12
 
0.2%
-8.142 8
 
0.1%
-10.172 6
 
0.1%
-3.728 6
 
0.1%
-4.204 6
 
0.1%
-5.131 6
 
0.1%
-6.928 5
 
0.1%
-5.18 5
 
0.1%
-6.544 5
 
0.1%
-5.53 5
 
0.1%
Other values (4694) 6334
99.0%
ValueCountFrequency (%)
-46.655 1
< 0.1%
-41.568 1
< 0.1%
-41.408 1
< 0.1%
-40.198 1
< 0.1%
-39.295 1
< 0.1%
-39.286 1
< 0.1%
-39.104 1
< 0.1%
-39.064 1
< 0.1%
-38.92 1
< 0.1%
-38.793 1
< 0.1%
ValueCountFrequency (%)
-0.149 1
< 0.1%
-0.155 1
< 0.1%
-0.223 1
< 0.1%
-0.591 1
< 0.1%
-0.716 1
< 0.1%
-0.755 1
< 0.1%
-0.804 1
< 0.1%
-0.843 1
< 0.1%
-0.876 1
< 0.1%
-0.884 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
1
4130 
0
2268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6398
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

Length

2022-11-29T17:52:12.782547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:52:12.856162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

Most occurring characters

ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6398
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 6398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4130
64.6%
0 2268
35.4%

speechiness
Real number (ℝ)

Distinct1114
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098018318
Minimum0.0225
Maximum0.956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:12.937679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0225
5-th percentile0.0291
Q10.038825
median0.0572
Q30.112
95-th percentile0.32
Maximum0.956
Range0.9335
Interquartile range (IQR)0.073175

Descriptive statistics

Standard deviation0.097224313
Coefficient of variation (CV)0.99189942
Kurtosis7.1182157
Mean0.098018318
Median Absolute Deviation (MAD)0.0236
Skewness2.367078
Sum627.1212
Variance0.009452567
MonotonicityNot monotonic
2022-11-29T17:52:13.037421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0315 29
 
0.5%
0.0331 28
 
0.4%
0.106 23
 
0.4%
0.0344 23
 
0.4%
0.0336 21
 
0.3%
0.0338 21
 
0.3%
0.0296 21
 
0.3%
0.113 21
 
0.3%
0.0332 19
 
0.3%
0.0303 19
 
0.3%
Other values (1104) 6173
96.5%
ValueCountFrequency (%)
0.0225 1
< 0.1%
0.0231 1
< 0.1%
0.0232 2
< 0.1%
0.0234 1
< 0.1%
0.0236 1
< 0.1%
0.0237 2
< 0.1%
0.0239 1
< 0.1%
0.024 1
< 0.1%
0.0241 1
< 0.1%
0.0243 2
< 0.1%
ValueCountFrequency (%)
0.956 1
< 0.1%
0.91 1
< 0.1%
0.876 1
< 0.1%
0.854 1
< 0.1%
0.733 1
< 0.1%
0.691 1
< 0.1%
0.681 1
< 0.1%
0.655 1
< 0.1%
0.604 1
< 0.1%
0.599 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2668
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21692842
Minimum0
Maximum0.996
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:13.139562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.467 × 10-5
Q10.0085325
median0.06705
Q30.311
95-th percentile0.94
Maximum0.996
Range0.996
Interquartile range (IQR)0.3024675

Descriptive statistics

Standard deviation0.29683498
Coefficient of variation (CV)1.3683545
Kurtosis0.815446
Mean0.21692842
Median Absolute Deviation (MAD)0.0666155
Skewness1.4529776
Sum1387.908
Variance0.088111005
MonotonicityNot monotonic
2022-11-29T17:52:13.477244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 20
 
0.3%
0.993 17
 
0.3%
0.13 16
 
0.3%
0.892 16
 
0.3%
0.119 13
 
0.2%
0.193 13
 
0.2%
0.988 13
 
0.2%
0.129 12
 
0.2%
0.992 12
 
0.2%
0.983 12
 
0.2%
Other values (2658) 6254
97.7%
ValueCountFrequency (%)
0 5
0.1%
1.03 × 10-61
 
< 0.1%
1.08 × 10-61
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.19 × 10-61
 
< 0.1%
1.21 × 10-61
 
< 0.1%
1.29 × 10-61
 
< 0.1%
1.3 × 10-61
 
< 0.1%
1.33 × 10-61
 
< 0.1%
1.34 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 10
0.2%
0.995 20
0.3%
0.994 12
0.2%
0.993 17
0.3%
0.992 12
0.2%
0.991 8
 
0.1%
0.99 8
 
0.1%
0.989 9
0.1%
0.988 13
0.2%
0.987 8
 
0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2302
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16529271
Minimum0
Maximum0.995
Zeros2406
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:13.579461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.665 × 10-5
Q30.05765
95-th percentile0.907
Maximum0.995
Range0.995
Interquartile range (IQR)0.05765

Descriptive statistics

Standard deviation0.31873595
Coefficient of variation (CV)1.9283123
Kurtosis0.88668913
Mean0.16529271
Median Absolute Deviation (MAD)1.665 × 10-5
Skewness1.6297342
Sum1057.5427
Variance0.10159261
MonotonicityNot monotonic
2022-11-29T17:52:13.681744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2406
37.6%
0.553 13
 
0.2%
0.903 12
 
0.2%
0.905 10
 
0.2%
0.858 10
 
0.2%
0.927 10
 
0.2%
0.912 10
 
0.2%
0.945 10
 
0.2%
0.855 9
 
0.1%
0.936 9
 
0.1%
Other values (2292) 3899
60.9%
ValueCountFrequency (%)
0 2406
37.6%
1 × 10-63
 
< 0.1%
1.01 × 10-65
 
0.1%
1.02 × 10-62
 
< 0.1%
1.03 × 10-63
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-63
 
< 0.1%
1.07 × 10-64
 
0.1%
1.08 × 10-61
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.993 4
0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 2
< 0.1%

liveness
Real number (ℝ)

Distinct1206
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19669964
Minimum0.0167
Maximum0.982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:13.781966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0167
5-th percentile0.0606
Q10.0968
median0.126
Q30.249
95-th percentile0.552
Maximum0.982
Range0.9653
Interquartile range (IQR)0.1522

Descriptive statistics

Standard deviation0.16614849
Coefficient of variation (CV)0.84468121
Kurtosis5.675475
Mean0.19669964
Median Absolute Deviation (MAD)0.04565
Skewness2.2388502
Sum1258.4843
Variance0.027605321
MonotonicityNot monotonic
2022-11-29T17:52:13.875651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 97
 
1.5%
0.109 80
 
1.3%
0.11 78
 
1.2%
0.106 76
 
1.2%
0.112 75
 
1.2%
0.114 71
 
1.1%
0.104 69
 
1.1%
0.105 68
 
1.1%
0.108 67
 
1.0%
0.107 57
 
0.9%
Other values (1196) 5660
88.5%
ValueCountFrequency (%)
0.0167 1
< 0.1%
0.0199 1
< 0.1%
0.0206 1
< 0.1%
0.0208 1
< 0.1%
0.021 1
< 0.1%
0.0215 1
< 0.1%
0.0217 1
< 0.1%
0.0219 1
< 0.1%
0.0229 1
< 0.1%
0.0235 1
< 0.1%
ValueCountFrequency (%)
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%
0.976 3
< 0.1%
0.975 1
 
< 0.1%
0.973 1
 
< 0.1%
0.971 3
< 0.1%
0.97 2
< 0.1%

valence
Real number (ℝ)

Distinct1219
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44373425
Minimum0
Maximum0.976
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:13.977619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.060385
Q10.24
median0.434
Q30.628
95-th percentile0.867
Maximum0.976
Range0.976
Interquartile range (IQR)0.388

Descriptive statistics

Standard deviation0.24577573
Coefficient of variation (CV)0.55388046
Kurtosis-0.90645069
Mean0.44373425
Median Absolute Deviation (MAD)0.194
Skewness0.17783336
Sum2839.0117
Variance0.060405709
MonotonicityNot monotonic
2022-11-29T17:52:14.079419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.534 20
 
0.3%
0.594 17
 
0.3%
0.523 16
 
0.3%
0.538 16
 
0.3%
0.575 16
 
0.3%
0.356 16
 
0.3%
0.288 16
 
0.3%
0.437 16
 
0.3%
0.198 16
 
0.3%
0.625 15
 
0.2%
Other values (1209) 6234
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.0207 1
< 0.1%
0.0216 1
< 0.1%
0.0224 1
< 0.1%
0.0226 1
< 0.1%
0.0251 1
< 0.1%
0.0259 1
< 0.1%
0.0269 1
< 0.1%
0.027 1
< 0.1%
0.0275 1
< 0.1%
ValueCountFrequency (%)
0.976 2
 
< 0.1%
0.975 1
 
< 0.1%
0.974 1
 
< 0.1%
0.973 3
 
< 0.1%
0.969 1
 
< 0.1%
0.968 4
 
0.1%
0.967 3
 
< 0.1%
0.966 3
 
< 0.1%
0.965 9
0.1%
0.964 11
0.2%

tempo
Real number (ℝ)

Distinct5531
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.35387
Minimum39.369
Maximum210.977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:14.181635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39.369
5-th percentile77.6478
Q198.09125
median121.07
Q3141.085
95-th percentile176.0172
Maximum210.977
Range171.608
Interquartile range (IQR)42.99375

Descriptive statistics

Standard deviation29.847389
Coefficient of variation (CV)0.24394315
Kurtosis-0.47850239
Mean122.35387
Median Absolute Deviation (MAD)21.1195
Skewness0.32304108
Sum782820.07
Variance890.86662
MonotonicityNot monotonic
2022-11-29T17:52:14.273702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.993 14
 
0.2%
142.187 12
 
0.2%
92.003 6
 
0.1%
120.012 6
 
0.1%
119.961 6
 
0.1%
122.997 6
 
0.1%
100.005 6
 
0.1%
125.991 5
 
0.1%
100.026 5
 
0.1%
129.988 5
 
0.1%
Other values (5521) 6327
98.9%
ValueCountFrequency (%)
39.369 1
< 0.1%
40.645 1
< 0.1%
48.718 1
< 0.1%
48.888 1
< 0.1%
49.092 1
< 0.1%
49.762 1
< 0.1%
52.532 1
< 0.1%
54.041 1
< 0.1%
56.16 1
< 0.1%
57.257 1
< 0.1%
ValueCountFrequency (%)
210.977 1
< 0.1%
208.067 1
< 0.1%
207.111 1
< 0.1%
206.097 1
< 0.1%
205.999 1
< 0.1%
205.972 1
< 0.1%
205.846 1
< 0.1%
205.57 1
< 0.1%
204.034 1
< 0.1%
204.002 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct5591
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236704.21
Minimum29853
Maximum1734201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:14.375277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum29853
5-th percentile140554.8
Q1193206.75
median221246.5
Q3259316.5
95-th percentile386000
Maximum1734201
Range1704348
Interquartile range (IQR)66109.75

Descriptive statistics

Standard deviation85636.984
Coefficient of variation (CV)0.36178902
Kurtosis28.162537
Mean236704.21
Median Absolute Deviation (MAD)31402
Skewness3.3725241
Sum1.5144335 × 109
Variance7.3336931 × 109
MonotonicityNot monotonic
2022-11-29T17:52:14.476745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321853 12
 
0.2%
386000 8
 
0.1%
240000 7
 
0.1%
208000 6
 
0.1%
345854 6
 
0.1%
235200 5
 
0.1%
180000 5
 
0.1%
213500 5
 
0.1%
227533 4
 
0.1%
189467 4
 
0.1%
Other values (5581) 6336
99.0%
ValueCountFrequency (%)
29853 2
< 0.1%
37303 1
< 0.1%
46133 1
< 0.1%
47280 1
< 0.1%
50600 1
< 0.1%
52013 1
< 0.1%
55800 1
< 0.1%
55893 1
< 0.1%
57307 1
< 0.1%
59387 1
< 0.1%
ValueCountFrequency (%)
1734201 1
< 0.1%
1146928 1
< 0.1%
1125720 1
< 0.1%
1004627 1
< 0.1%
1003747 1
< 0.1%
941926 1
< 0.1%
923455 1
< 0.1%
910213 1
< 0.1%
832067 1
< 0.1%
827322 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
4
5799 
3
 
436
5
 
121
1
 
41
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6398
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

Length

2022-11-29T17:52:14.576372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:52:14.650211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6398
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 6398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5799
90.6%
3 436
 
6.8%
5 121
 
1.9%
1 41
 
0.6%
0 1
 
< 0.1%

chorus_hit
Real number (ℝ)

Distinct6241
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.028399
Minimum0
Maximum213.15499
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:14.731648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.110201
Q128.059135
median36.265365
Q348.292538
95-th percentile78.882455
Maximum213.15499
Range213.15499
Interquartile range (IQR)20.233403

Descriptive statistics

Standard deviation19.568827
Coefficient of variation (CV)0.4769581
Kurtosis7.1122099
Mean41.028399
Median Absolute Deviation (MAD)9.60786
Skewness2.0314518
Sum262499.69
Variance382.93899
MonotonicityNot monotonic
2022-11-29T17:52:14.823512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.94077 12
 
0.2%
58.49512 8
 
0.1%
0 7
 
0.1%
97.5751 6
 
0.1%
30.50348 5
 
0.1%
26.76235 5
 
0.1%
29.55778 3
 
< 0.1%
47.70647 3
 
< 0.1%
41.37868 3
 
< 0.1%
22.47198 3
 
< 0.1%
Other values (6231) 6343
99.1%
ValueCountFrequency (%)
0 7
0.1%
6.19054 1
 
< 0.1%
8.71471 1
 
< 0.1%
9.39184 1
 
< 0.1%
9.75479 1
 
< 0.1%
9.97945 1
 
< 0.1%
10.79435 1
 
< 0.1%
11.77365 1
 
< 0.1%
12.91193 1
 
< 0.1%
13.15085 1
 
< 0.1%
ValueCountFrequency (%)
213.15499 1
< 0.1%
185.9394 1
< 0.1%
185.49087 1
< 0.1%
177.13918 1
< 0.1%
176.72857 1
< 0.1%
173.5694 1
< 0.1%
173.39504 1
< 0.1%
169.99079 1
< 0.1%
158.30675 1
< 0.1%
158.22827 1
< 0.1%

sections
Real number (ℝ)

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.316505
Minimum2
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2022-11-29T17:52:14.923056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median10
Q312
95-th percentile16
Maximum88
Range86
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7760109
Coefficient of variation (CV)0.36601648
Kurtosis40.023462
Mean10.316505
Median Absolute Deviation (MAD)2
Skewness3.4287305
Sum66005
Variance14.258258
MonotonicityNot monotonic
2022-11-29T17:52:15.006540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9 979
15.3%
10 874
13.7%
11 815
12.7%
8 768
12.0%
12 610
9.5%
7 551
8.6%
13 402
6.3%
6 317
 
5.0%
14 262
 
4.1%
5 166
 
2.6%
Other values (30) 654
10.2%
ValueCountFrequency (%)
2 7
 
0.1%
3 25
 
0.4%
4 76
 
1.2%
5 166
 
2.6%
6 317
 
5.0%
7 551
8.6%
8 768
12.0%
9 979
15.3%
10 874
13.7%
11 815
12.7%
ValueCountFrequency (%)
88 1
 
< 0.1%
57 1
 
< 0.1%
49 1
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
37 1
 
< 0.1%
33 3
< 0.1%
32 3
< 0.1%

target
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
1
3199 
0
3199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6398
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Length

2022-11-29T17:52:15.088112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:52:15.159234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Most occurring characters

ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6398
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3199
50.0%
0 3199
50.0%

Interactions

2022-11-29T17:52:10.258319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:55.803410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.891152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.546301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.687640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.922693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.042197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.191722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.448402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.551213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.654567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.773705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.139922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.342636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:55.872602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.975802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.630963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.781403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.007357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.126877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.260273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.533098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.635868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.739217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.874160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.224570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.427313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:55.957243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:57.620824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.715660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.850680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.092012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.226821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.344942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.617766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.720527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.823899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.958675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.309209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.496366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.041960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:57.702519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.800461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.935292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.185781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.311483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.429601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.702417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.805185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.908550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.043153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.393859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.581043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.135739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:57.791043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.885098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.020090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.254819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.389950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.514267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.780571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.898953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.986699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.127637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.478553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.665694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.204953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:57.872313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.978870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.104756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.355101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.474219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.598884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.865250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.970534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.071527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.227913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.563197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.743836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.305244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:57.960468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.085656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.189412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.424158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.574501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.692662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.949874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.052846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.156352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.312557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.641350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.828522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.389895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.045124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.185945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.267566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.508896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.659153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.777305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.034514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.137511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.256602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.604570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.725832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.928797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.474557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.129639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.264082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.352235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.593553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.743556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.861950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.134783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.222146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.341261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.696081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.826293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.997809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.559212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.214213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.348768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.568448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.693856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.828197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.946630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.219080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.306792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.425909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.777266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.910443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:11.082506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.643862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.298838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.433419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.668715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.771992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:02.912872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.194115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.303755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.407076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.510576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.870683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.995116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:11.182815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.737625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.399135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.518152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.769007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.872446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.013176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.278756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.397536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.500844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.610873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:08.972207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.095376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:11.267450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:56.806680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:58.477265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:59.602990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:00.854102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:01.957119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:03.097803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:04.363404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:05.466769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:06.569877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:07.689016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:09.061788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:52:10.180018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:52:15.230735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:52:15.381364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:52:15.534417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:52:15.679260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:52:15.811814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:52:15.903762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:52:11.398977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:52:11.601413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Wild ThingsAlessia Caraspotify:track:2ZyuwVvV6Z3XJaXIFbspeE0.7410.6261-4.82600.08860.020000.00000.08280.706108.029188493441.18681101
1SurfboardEsquivel!spotify:track:61APOtq25SCMuK0V5w2Kgp0.4470.2475-14.66100.03460.871000.81400.09460.250155.489176880333.1808390
2Love SomeoneLukas Grahamspotify:track:2JqnpexlO9dmvjUMCaLCLJ0.5500.4159-6.55700.05200.161000.00000.10800.274172.065205463444.8914791
3Music To My Ears (feat. Tory Lanez)Keys N Kratesspotify:track:0cjfLhk8WJ3etPTCseKXtk0.5020.6480-5.69800.05270.005130.00000.20400.29191.837193043429.5252170
4Juju On That Beat (TZ Anthem)Zay Hilfigerrr & Zayion McCallspotify:track:1lItf5ZXJc1by9SbPeljFd0.8070.8871-3.89210.27500.003810.00000.39100.780160.517144244424.9919981
5Here's To Never Growing UpAvril Lavignespotify:track:0qwcGscxUHGZTgq0zcaqk10.4820.8730-3.14510.08530.011100.00000.40900.737165.084214320432.17301121
6Sex Metal BarbieIn This Momentspotify:track:75BGF4LC7AOLFfxn6ukZDH0.5330.9350-3.70410.12800.013900.00000.16800.481140.092262493421.04510140
7Helluva NightLudacrisspotify:track:0flKDWZq11997Fb2ptkQvu0.7360.5222-8.02010.11600.029900.00000.10800.36997.547200387460.21027101
8Holiday With HHNo Brosspotify:track:7LBa0KNFR8IY3g7LOfXqu80.1660.9857-2.88610.17000.001830.01420.95800.139174.725252787431.23583110
9My LastBig Sean Featuring Chris Brownspotify:track:70tFuqBcduJv15bEnOPRTh0.3870.7738-5.68510.17000.098000.00000.20900.36878.629254120423.3024591
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
6388Strip That DownLiam Payne Featuring Quavospotify:track:4Ro98RCK90oHqqSZUnTFq50.8740.4976-5.48410.05620.22000.0000000.07610.5420106.023204502430.10821101
6389What NowRihannaspotify:track:0aUWfpD3PlSv3FTTKcT2rN0.4020.6968-4.79900.04480.04760.0000000.62800.2270180.158243093331.53643111
6390Tear In My Hearttwenty one pilotsspotify:track:3bnVBN67NBEzedqQuWrpP40.6550.6322-4.80210.04890.01890.0000000.07220.4470120.113188493451.5280481
6391Sweater WeatherThe Neighbourhoodspotify:track:2QjOHCTQ1Jl3zawyYOpxh60.6120.80710-2.81010.03360.04950.0177000.10100.3980124.053240400491.2055271
6392UntouchableYoungBoy Never Broke Againspotify:track:4MofYf0f4ijlVV6elUW5S30.7800.7841-5.03910.18600.04470.0000000.12200.430085.023180706446.6227781
6393Lotus FlowersYoltaspotify:track:4t1TljQWJ6ZuoSY67zVvBI0.1720.3589-14.43010.03420.88600.9660000.31400.036172.272150857424.3082470
6394Calling My SpiritKodak Blackspotify:track:2MShy1GSSgbmGUxADNIao50.9100.3661-9.95410.09410.09960.0000000.26100.7400119.985152000432.5385681
6395Teenage DreamKaty Perryspotify:track:55qBw1900pZKfXJ6Q9A2Lc0.7190.80410-4.58110.03550.01320.0000030.13900.6050119.999227760420.7337171
6396Stormy WeatherOscar Petersonspotify:track:4o9npmYHrOF1rUxxTVH8h40.6000.1777-16.07010.05610.98900.8680000.14900.5600120.030213387421.65301140
6397DustHans Zimmerspotify:track:2khIaVUkbMmDHB596lyMG30.1210.1234-23.02500.04430.96400.6960000.10300.029795.182341396471.05343150